To be effective, solo data analysts or engineers need to focus on refining algorithms and analyzing data, not building data pipelines.

A brief explanation of how data integration continues to evolve.

Five differences between using an ETL platform vs. writing your own code: The ETL (extract, transform, load) process is one of the most critical, and one of the most challenging, parts of enterprise data integration. But what if we told you there was a low-code ETL solution to your problems?

In today’s digital age, robust and faster data analytics is essential for your organization’s growth and success. The faster you deliver analytics-ready data to your analyst, the faster they can analyze and derive insights. Though you would have adopted the ELT process with EL data pipelines to load data quickly to the warehouse, your team would still face inefficient and delayed analysis.

ETL stands for Extract, Transform, and Load. It is defined as a Data Integration service and allows companies to combine data from various sources into a single, consistent data store that is loaded into a Data Warehouse or any other target system. ETL serves as the foundation for Machine Learning and Data Analytics workstreams. Through multiple business rules, ETL organizes and cleanses data in a way that caters to the Business Intelligence needs, like monthly reporting.